# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================
"""ResNet variants"""
import math
import mindspore
from mindspore.common import initializer as init
from mindspore import nn,ops
from mindspore.ops import operations as P
from mindspore.train.serialization import load_param_into_net
from mindspore.nn import Cell
from src.models.splat import SplAtConv2d
from mindspore.nn import Conv2d,Cell,Dense,BatchNorm2d,ReLU
from src.models.splat import rSoftMax
from src.models.utils import Resume

class GlobalAvgPool2d(nn.Cell):
    """Global average pooling layer"""
    def __init__(self, input_size):
        """Global average pooling over the input's spatial dimensions"""
        super(GlobalAvgPool2d, self).__init__()
        self.AvgPool2d = nn.AvgPool2d(input_size, stride=1)
        self.reshape = P.Reshape()

    def construct(self, x):
        output = self.AvgPool2d(x)
        output = self.reshape(output, ((x.shape)[0], -1))
        return output

class Bottleneck(nn.Cell):
    """ResNet Bottleneck"""
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 radix=0, cardinality=1, bottleneck_width=64,
                 avd=False, avd_first=False, dilation=1, is_first=None,
                 rectified_conv=False, rectify_avg=False,
                 norm_layer=None, last_gamma=False):
        super(Bottleneck, self).__init__()
        group_width = int(planes * (bottleneck_width / 64.)) * cardinality
        self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, has_bias=False)
        self.bn1 = norm_layer(group_width)
        self.radix = radix
        self.avd = avd and (stride > 1 or is_first)
        self.avd_first = avd_first

        if self.avd:
            self.pad = nn.Pad(paddings=((0, 0), (0, 0), (1, 1), (1, 1)), mode="CONSTANT")
            self.avd_layer = nn.AvgPool2d(3, stride)
            stride = 1

        if radix >= 1:
            self.conv2 = SplAtConv2d(
                group_width, group_width, kernel_size=3,
                stride=stride, padding=dilation, pad_mode="pad",
                dilation=dilation, groups=cardinality, bias=False,
                radix=radix, rectify=rectified_conv,
                rectify_avg=rectify_avg,
                norm_layer=norm_layer)
        else:
            self.conv2 = nn.Conv2d(
                group_width, group_width, kernel_size=3, stride=stride,
                padding=dilation, pad_mode="pad", dilation=dilation,
                group=cardinality, has_bias=False)
            self.bn2 = norm_layer(group_width)

        self.conv3 = nn.Conv2d(
            group_width, planes * 4, kernel_size=1, has_bias=False)
        self.bn3 = norm_layer(planes*4)

        if last_gamma:
            self.bn3.gamma.set_data(init.initializer('zeros', self.bn3.gamma.shape))

        self.relu = nn.ReLU()
        self.downsample = downsample
        self.dilation = dilation
        self.stride = stride

    def construct(self, x):
        """Bottleneck construct"""
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        if self.avd and self.avd_first:
            out = self.avd_layer(out)
        out = self.conv2(out)
        if self.radix == 0:
            out = self.bn2(out)
            out = self.relu(out)
        if self.avd and not self.avd_first:
            out = self.pad(out)
            out = self.avd_layer(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)

        return out

class ResNet(nn.Cell):
    """
    ResNet Variants
    Parameters
    ---------
    block: Block
        Class for the residual block. Options are BasicBlockV1, BottleneckV1.
    layers : list of int
        Numbers of layers in each block
    classes : int, default 1000
        Number of classification classes.
    dilated : bool, default False
        Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
        typically used in Semantic Segmentation.
    norm_layer : object
        Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
        for Synchronized Cross-GPU BachNormalization).

    Reference:
        - He, Kaiming, et al. "Deep residual learning for image recognition.
        - " Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
        - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
    """
    def __init__(self, block, layers, radix=1, groups=1, bottleneck_width=64,
                 num_classes=1000, dilated=False, dilation=1,
                 deep_stem=False, stem_width=64, avg_down=False,
                 rectified_conv=False, rectify_avg=False,
                 avd=False, avd_first=False,
                 final_drop=0.0, last_gamma=False, norm_layer=nn.BatchNorm2d):
        self.cardinality = groups
        self.bottleneck_width = bottleneck_width
        # ResNet-D params
        self.inplanes = stem_width*2 if deep_stem else 64
        self.avg_down = avg_down
        self.last_gamma = last_gamma
        # ResNeSt params
        self.radix = radix
        self.avd = avd
        self.avd_first = avd_first

        super(ResNet, self).__init__()
        self.rectified_conv = rectified_conv
        self.rectify_avg = rectify_avg
        conv_layer = nn.Conv2d
        conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}

        if deep_stem:
            self.conv1 = nn.SequentialCell(
                conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1,
                           pad_mode="pad", has_bias=False, **conv_kwargs),
                norm_layer(stem_width),
                nn.ReLU(),
                conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1,
                           pad_mode="pad", has_bias=False, **conv_kwargs),
                norm_layer(stem_width),
                nn.ReLU(),
                conv_layer(stem_width, stem_width*2, kernel_size=3, stride=1, padding=1,
                           pad_mode="pad", has_bias=False, **conv_kwargs),
            )
        else:
            self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3, pad_mode="pad",
                                    has_bias=False, **conv_kwargs)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU()
        self.pad = nn.Pad(paddings=((0, 0), (0, 0), (1, 1), (1, 1)), mode="CONSTANT")
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
        self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
        if dilated or dilation == 4:
            self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
                                           dilation=2, norm_layer=norm_layer)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
                                           dilation=4, norm_layer=norm_layer)
        elif dilation == 2:
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                           dilation=1, norm_layer=norm_layer)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
                                           dilation=2, norm_layer=norm_layer)
        else:
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                           norm_layer=norm_layer)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                           norm_layer=norm_layer)
        self.drop = nn.Dropout(final_drop) if final_drop > 0.0 else None
        self.fc = nn.Dense(512 * block.expansion, num_classes)
        self.out_channels = 512 * block.expansion

        for m in self.cells_and_names():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, (norm_layer, nn.BatchNorm2d)):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
                    norm_layer=None, is_first=True):
        """__make__layers"""
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            down_layers = []
            if self.avg_down:
                if dilation == 1:
                    down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride))
                else:
                    down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1))
                down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
                                             kernel_size=1, stride=1, has_bias=False))
            else:
                down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
                                             kernel_size=1, stride=stride, has_bias=False))
            down_layers.append(norm_layer(planes * block.expansion))
            downsample = nn.SequentialCell(*down_layers)

        layers = []
        if dilation in (1, 2):
            layers.append(block(self.inplanes, planes, stride, downsample=downsample,
                                radix=self.radix, cardinality=self.cardinality,
                                bottleneck_width=self.bottleneck_width,
                                avd=self.avd, avd_first=self.avd_first,
                                dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
                                rectify_avg=self.rectify_avg,
                                norm_layer=norm_layer, last_gamma=self.last_gamma))
        elif dilation == 4:
            layers.append(block(self.inplanes, planes, stride, downsample=downsample,
                                radix=self.radix, cardinality=self.cardinality,
                                bottleneck_width=self.bottleneck_width,
                                avd=self.avd, avd_first=self.avd_first,
                                dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,
                                rectify_avg=self.rectify_avg,
                                norm_layer=norm_layer, last_gamma=self.last_gamma))
        else:
            raise RuntimeError("=> unknown dilation size: {}".format(dilation))

        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            assert 0 <= i <= blocks
            layers.append(block(self.inplanes, planes,
                                radix=self.radix, cardinality=self.cardinality,
                                bottleneck_width=self.bottleneck_width,
                                avd=self.avd, avd_first=self.avd_first,
                                dilation=dilation, rectified_conv=self.rectified_conv,
                                rectify_avg=self.rectify_avg,
                                norm_layer=norm_layer, last_gamma=self.last_gamma))

        return nn.SequentialCell(*layers)

    def construct(self, x):
        """ResNeSt construct"""
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.pad(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        avgpool = GlobalAvgPool2d(x.shape[-2])
        x = avgpool(x)
        flatten = P.Flatten()
        x = flatten(x)
        if self.drop:
            x = self.drop(x)
        x = self.fc(x)

        return x

def resnet50(pretrained=False, root='~/.encoding/models', **kwargs):
    """
    Construct a ResNet-50 model
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        param = Resume(model, root)
        load_param_into_net(model, param)
    return model

def resnet101(pretrained=False, root='~/.encoding/models', **kwargs):
    """
    Construct a ResNet-101 model
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        param = Resume(model, root)
        load_param_into_net(model, param)
    return model

def resnet152(pretrained=False, root='~/.encoding/models', **kwargs):
    """
    Construct a ResNet-152 model
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        param = Resume(model, root)
        load_param_into_net(model, param)
    return model

